Recommender systems are known for example from electronic program guides, where they are used to reduce input about a large number of available programs to a selection of a more limited number of possible viewing choices and to present these choices to a user. User-rating-based recommender systems gather rating data for individual choices from users and use this rating data to direct the selection of possible choices. A predicted rating of an item by a user may be computed by weighting ratings form different users for the item according to similarity between the users. In collaborative filtering, collected ratings from different users for the same items are used to search for correlations between users. More generally, a user-rating-based recommender system is designed to make user-dependent selections from sets of items or ordered lists of items, based on rating input collected from different users.
A recommender system that makes use of social network information is described in (Yang, X., Y. Guo, and Y. Liu [2011]. Bayesian-inference Based Recommendation in Online Social Networks, in Proceedings of the International Conference on Computer Communications, IEEE INFOCOM 2011, Shanghai, China, Apr. 10-15, 2011, pp. 551-555 . The authors propose to compute predicted ratings with weights selected dependent on the closeness of users in a social network. The article describes that a user can also query his friends to obtain ratings of a specific movie, or that ratings from friends of friends in the social network can be used, but with less weight.
In each case the quality of user-rating-based recommender systems depends on the availability of ratings provided by users. If no rating from any user is available for an item, no rating-based-recommendation of the item is possible. With available ratings from increasing numbers of users, increasingly more reliable computations of recommendations become possible